We have proposed SloTTAr, a novel approach to learning action segments belonging to modular sub-routines (suitable as `options' [1]}) along with the number of such sub-routines in a mostly unsupervised fashion.
Our approach draws insight from prior literature on learning about visual objects [2]}, Transformers [3]} and adaptive computation [4]} to improve over existing purely sequential approaches.
We found that SloTTAr outperforms CompILE and OMPN in terms of recovering ground-truth sub-routine segments across a wide range environments in Craft [5]} and Minigrid [6]} that have a fixed number of sub-routines.
On more sophisticated environments with trajectories containing varying numbers of sub-routines, it could also be observed how SloTTAr outperforms CompILE and remains competitive with OMNP, despite these baseline models requiring ground-truth information about the number of sub-routines at the level of individual trajectories.
At the same time, these results also indicated how current state-of-the-art approaches are still limited in their ability to decompose action sequences into semantically meaningful modular sub-routines.
Our analysis revealed how SloTTAr leverages parallel access to the full sequence to perform sequence decomposition, which further leads to a substantial speed-up through leveraging parallel computation.
In this way, we demonstrated how general principles of similarity-based grouping used to segment visual inputs are also relevant for grouping other input modalities, suggesting many additional avenues for future research.
